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The Hard Problem of Prediction for Conflict Prevention

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  • Mueller, Hannes
  • Rauh, Christopher

Abstract

There is a growing interest in better conflict prevention and this provides a strong motivation for better conflict forecasting. A key problem of conflict forecasting for prevention is that predicting the start of conflict in previously peaceful countries is extremely hard. To make progress in this hard problem this project exploits both supervised and unsupervised machine learning. Specifically, the latent Dirichlet allocation (LDA) model is used for feature extraction from 3.8 million newspaper articles and these features are then used in a random forest model to predict conflict. We find that forecasting hard cases is possible and benefits from supervised learning despite the small sample size. Several topics are negatively associated with the outbreak of conflict and these gain importance when predicting hard onsets. The trees in the random forest use the topics in lower nodes where they are evaluated conditionally on conflict history, which allows the random forest to adapt to the hard problem and provides useful forecasts for prevention.

Suggested Citation

  • Mueller, Hannes & Rauh, Christopher, 2019. "The Hard Problem of Prediction for Conflict Prevention," CEPR Discussion Papers 13748, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:13748
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    Cited by:

    1. Hannes Mueller & Christopher Rauh, 2022. "Using past violence and current news to predict changes in violence," International Interactions, Taylor & Francis Journals, vol. 48(4), pages 579-596, July.
    2. Mueller, H. & Rauh, C., 2022. "Building Bridges to Peace: A Quantitative Evaluation of Power-Sharing Agreements," Cambridge Working Papers in Economics 2261, Faculty of Economics, University of Cambridge.
    3. Mark Musumba & Naureen Fatema & Shahriar Kibriya, 2021. "Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    4. Alonso-Alvarez, Irma & Molina, Luis, 2023. "How to foresee crises? A new synthetic index of vulnerabilities for emerging economies," Economic Modelling, Elsevier, vol. 125(C).
    5. Sidney Michelini & Barbora Šedová & Jacob Schewe & Katja Frieler, 2023. "Extreme weather impacts do not improve conflict predictions in Africa," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    6. Mueller,Hannes Felix & Techasunthornwat,Chanon, 2020. "Conflict and Poverty," Policy Research Working Paper Series 9455, The World Bank.
    7. Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.

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    More about this item

    Keywords

    Armed conflict; Forecasting; Newspaper text; Machine learning; Topic models; Random forest;
    All these keywords.

    JEL classification:

    • F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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